Is it possible to measure social entrepreneurship in firms?

[EN] This study defines and proposes a measurement scale for social entrepreneurship (SE) in its broadest sense. The broad definition of SE covers for-profit firms that use social aims as a core component of their strategy. By pursuing social aims, these firms can boost the value of their products or services for consumers or exploit new business areas. Under this broad definition of SE, profit-seeking and the pursuit of social aims converge, thereby revealing a form of SE that has received little attention in either theoretical or empirical research. To fill this research gap, the present study proposes a measurement scale to measure broad SE in firms. The process used to build the scale draws upon research by Churchill (1979) and DeVellis (1991) and combines the Delphi technique, a pre-test questionnaire and structural equation modelling. The main aim of this research is to develop a scale capable of measuring broad SE in firms. The theoretical basis for the scale is supported by an empirical study in the hotel sector. The scale provides a valid, reliable instrument for measuring broad SE in firms. The scale meets all sociometric properties required of measurement scales in the social sciences, namely dimensionality, reliability and validity.

On the Stability and Equilibrium Points of Multistaged SI (n) R Epidemic Models

This paper relies on the concept of next generation matrix defined ad hoc for a new proposed extended SEIR model referred to as SI(n)R-model to study its stability. The model includes n successive stages of infectious subpopulations, each one acting at the exposed subpopulation of the next infectious stage in a cascade global disposal where each infectious population acts as the exposed subpopulation of the next infectious stage. The model also has internal delays which characterize the time intervals of the coupling of the susceptible dynamics with the infectious populations of the various cascade infectious stages. Since the susceptible subpopulation is common, and then unique, to all the infectious stages, its coupled dynamic action on each of those stages is modeled with an increasing delay as the infectious stage index increases from 1 to n. The physical interpretation of the model is that the dynamics of the disease exhibits different stages in which the infectivity and the mortality rates vary as the individual numbers go through the process of recovery, each stage with a characteristic average time.